In [1]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import random
import glob
from skimage.feature import hog
from sklearn.preprocessing import StandardScaler
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
In [2]:
vehicle_image_location=glob.glob('./data/vehicles/*/*.png')
non_vehicle_image_location=glob.glob('./data/non-vehicles/*/*.png')
vehicle_images=[]
non_vehicle_images=[]
for fname in vehicle_image_location:
vehicle=mpimg.imread(fname)
vehicle_images.append(vehicle)
for fname in non_vehicle_image_location:
non_vehicle=mpimg.imread(fname)
non_vehicle_images.append(non_vehicle)
print('Number of images in vehicle dataset:',len(vehicle_images))
print()
print('Number of images in Non-vehicle dataset:',len(non_vehicle_images))
Color Histogram Features¶
In [41]:
def color_hist(img,nbins=32,bin_range=(0,256),vis=False):
channel_1_hist=np.histogram(img[:,:,0],nbins,bin_range)
channel_2_hist=np.histogram(img[:,:,1],nbins,bin_range)
channel_3_hist=np.histogram(img[:,:,2],nbins,bin_range)
bin_edges=channel_1_hist[1]
bin_centers=(bin_edges[1:]+bin_edges[0:len(bin_edges)-1])/2
color_hist_features=np.concatenate((channel_1_hist[0],channel_2_hist[0],channel_3_hist[0]))
if vis==True:
return channel_1_hist, channel_2_hist, channel_3_hist, bin_centers
else:
return color_hist_features
In [57]:
#Visualization of Color Histogram features
random_ind=np.random.randint(0,len(vehicle_images))
image=mpimg.imread('./examples/cutout1.jpg')
rh, gh, bh, bincen = color_hist(image, nbins=32,vis=True)
# Plot a figure with all three bar charts
if rh is not None:
fig = plt.figure(figsize=(12,3));
plt.subplot(141)
plt.imshow(image)
plt.title('Original Image', fontsize=15)
plt.subplot(142)
plt.bar(bincen, rh[0])
plt.xlim(0, 256)
plt.title('R Histogram', fontsize=15);
plt.subplot(143)
plt.bar(bincen, gh[0])
plt.xlim(0, 256)
plt.title('G Histogram', fontsize=15);
plt.subplot(144)
plt.bar(bincen, bh[0])
plt.xlim(0, 256)
plt.title('B Histogram', fontsize=15);
plt.savefig('output_images/color_hist_vis.png',
bbox_inches="tight")
else:
print('Your function is returning None for at least one variable...')
Binary Spatial Features¶
In [64]:
def bin_spatial(img,size=(32,32)):
binary_features=cv2.resize(img,size).ravel()
return binary_features
#Visualization
fig,axs=plt.subplots(1,2,figsize=(12,3))
ax=axs.ravel()
ax[0].plot(bin_spatial(image,size=(32,32)))
ax[0].set_title('Spatial_Size=(32,32)')
ax[1].plot(bin_spatial(image,size=(16,16)))
ax[1].set_title('Spatial_Size=(16,16)')
Out[64]:
HOG features¶
In [6]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block,vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
hog_features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), block_norm= 'L2-Hys',
transform_sqrt=True,
visualize=vis, feature_vector=feature_vec)
return hog_features, hog_image
# Otherwise call with one output
else:
hog_features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), block_norm= 'L2-Hys',
transform_sqrt=True,
visualize=vis, feature_vector=feature_vec)
return hog_features
In [65]:
#Visualize HOG features
features, hog_image = get_hog_features(image, orient= 8,pix_per_cell= 8, cell_per_block= 2,vis=True, feature_vec=False)
# Plot the examples
fig = plt.figure()
plt.subplot(121)
plt.imshow(image, cmap='gray')
plt.title('Example Car Image')
plt.subplot(122)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG Visualization')
Out[65]:
In [7]:
def extract_features(imgs,cspace='RGB',orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for image in imgs:
# Read in each one by one
#image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if cspace != 'RGB':
if cspace == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
binary_features=bin_spatial(feature_image,size=(spatial,spatial))
hist_features=color_hist(feature_image,bins)
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
features.append(np.concatenate((binary_features,hist_features,hog_features)))
#features.append(hog_features)
# Return list of feature vectors
return features
In [8]:
spatial=16
bins=16
colorspace = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
Train Classifier¶
In [9]:
t=time.time()
car_features = extract_features(vehicle_images,cspace=colorspace, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
hog_channel=hog_channel)
notcar_features = extract_features(non_vehicle_images,cspace=colorspace, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
hog_channel=hog_channel)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=rand_state)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X_train)
# Apply the scaler to X
X_train = X_scaler.transform(X_train)
X_test = X_scaler.transform(X_test)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
Hog Subsampling windows¶
In [10]:
#X_scaler = StandardScaler().fit(X_train)
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,spatial_size=spatial,hist_bins=bins,show_rects=False):
bbox_list=[]
draw_img = np.copy(img)
#img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
if cspace != 'RGB':
if cspace == 'HSV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
else: ctrans_tosearch = np.copy(img_tosearch)
#ctrans_tosearch = feature_image[ystart:ystop,:,:]
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step + 1
nysteps = (nyblocks - nblocks_per_window) // cells_per_step + 1
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=(spatial_size,spatial_size))
histogram_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, histogram_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1 or show_rects:#Inspired from Jeremy Shannon work
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
bbox_list.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
#cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
return bbox_list
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
random_color = False
# Iterate through the bounding boxes
for bbox in bboxes:
if color == 'random' or random_color: #Inspired form Jeremy Shannon Code
color = (np.random.randint(0,255), np.random.randint(0,255), np.random.randint(0,255))
random_color = True
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
In [11]:
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (255,0,0), 6)
# Return the image
return img
Visualize different Scales¶
In [97]:
spatial=16
bins=16
cspace = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
images=glob.glob('./test_images/*.jpg')
fig,axs=plt.subplots(3,2,figsize=(25,25))
ax=axs.ravel()
for idx,file in enumerate(images):
image=mpimg.imread(file)
img = image.astype(np.float32)/255
boxes=[]
ystart = 380
ystop = 550
scale = 1.0
out1=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out1)
ystart = 400
ystop = 600
scale = 1.5
out2=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out2)
ystart = 400
ystop = 656
scale = 2.0
out3=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out3)
rectangles = [item for sublist in boxes for item in sublist] #From Jeremy Shannon Work
rectangles= [ elem for elem in rectangles if elem[0][0] >= 600]
#Assuming that the car is in the left lane
#out_img=draw_boxes(image,rectangles,color='random')
heat = np.zeros_like(img[:,:,0]).astype(np.float)
heatmap=add_heat(heat,rectangles)
heatmap=apply_threshold(heatmap,2)
heatmap = np.clip(heatmap, 0, 255)
labels=label(heatmap)
out_img=draw_labeled_bboxes(image,labels)
ax[idx].imshow(out_img)
ax[idx].set_title('test image'+str(idx+1)+' with cars',fontsize=20)
ax[idx].axis('off')
plt.savefig('output_images/pipeline.png', bbox_inches="tight")
Image pipeline¶
In [98]:
def pipeline(image):
spatial=16
bins=16
cspace = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
boxes=[]
rectangles=[]
original_img=np.copy(image)
img=image.astype(np.float32)/255
ystart = 380
ystop = 550
scale = 1.0
out1=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out1)
ystart = 400
ystop = 600
scale = 1.5
out2=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out2)
ystart = 400
ystop = 656
scale = 2.0
out3=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out3)
rectangles = [item for sublist in boxes for item in sublist]
rectangles= [ elem for elem in rectangles if elem[0][0] >= 600]
heat = np.zeros_like(img[:,:,0]).astype(np.float)
heatmap=add_heat(heat,rectangles)
heatmap=apply_threshold(heatmap,2)
heatmap = np.clip(heatmap, 0, 255)
labels=label(heatmap)
out_img=draw_labeled_bboxes(original_img,labels)
return out_img
In [99]:
images=glob.glob('./test_images/*.jpg')
for file in images:
image=mpimg.imread(file)
out=pipeline(image)
fig,(ax1,ax2)=plt.subplots(1,2,figsize=(25,25))
fig.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image',fontsize=20)
ax2.imshow(out)
ax2.set_title('Image with Cars Drawn',fontsize=20)
In [23]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [24]:
test_output = 'test_video_output.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("test_video.mp4")
white_clip = clip1.fl_image(pipeline) #NOTE: this function expects color images!!
%time white_clip.write_videofile(test_output, audio=False)
In [25]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(test_output))
Out[25]:
Video Pipeline¶
In [26]:
#This one is taken from Jeremy Shannon work
class Vehicle_Detect():
def __init__(self):
# history of rectangles previous n frames
self.prev_rects = []
def add_rects(self, rects):
self.prev_rects.append(rects)
if len(self.prev_rects) > 15:
# throw out oldest rectangle set(s)
self.prev_rects = self.prev_rects[len(self.prev_rects)-15:]
In [29]:
def process_video(image):
spatial=16
bins=16
cspace = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 8
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
boxes=[]
rectangles=[]
original_img=np.copy(image)
img=image.astype(np.float32)/255
ystart = 380
ystop = 550
scale = 1.0
out1=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out1)
ystart = 400
ystop = 600
scale = 1.5
out2=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out2)
ystart = 400
ystop = 656
scale = 2.0
out3=find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,cspace,show_rects=False)
boxes.append(out3)
rectangles = [item for sublist in boxes for item in sublist]
rectangles= [ elem for elem in rectangles if elem[0][0] >= 600]
if len(rectangles) >0:
det.add_rects(rectangles)
heatmap = np.zeros_like(img[:,:,0]).astype(np.float)
for rect_set in det.prev_rects:
heatmap=add_heat(heatmap,rect_set)
heatmap=apply_threshold(heatmap,1+len(det.prev_rects)//2)
heatmap = np.clip(heatmap, 0, 255)
labels=label(heatmap)
out_img=draw_labeled_bboxes(original_img,labels)
return out_img
In [30]:
det=Vehicle_Detect()
test_output = 'project_video_output.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_video) #NOTE: this function expects color images!!
%time white_clip.write_videofile(test_output, audio=False)
In [31]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(test_output))
Out[31]: